Video surveillance is broadly applied in our daily life. When thousands of video cameras are deployed at every corner of a city and send captured images back to back-end control stations, image management and recognition becomes an arduous back-end task. Besides accomplishing the security purpose through manual monitoring, video surveillance may also be realized through intelligent video object detection. The stability of such a function directly affects the willingness of consumers to the acceptance of intelligent video cameras.
One of factors in the stability of intelligent video object detection is fast and accurate foreground detection. In a surveillance application, a foreground usually refers to a person, vehicle, or any other moving object on the scene. Accurate foreground detection can be applied in various surveillance applications, such as human tracking, human counting, and intrusion detection of virtual caution zone. Without a good foreground detection technique, aforementioned applications won't be able to provide satisfactory or commercializable result. Thus, accurate foreground detection is one of the most critical techniques.
In general foreground separation techniques, an image intensity background model is first established, and the foreground is then separated according to the difference between the foreground and the background. The most commonly adopted technique is to establish a background intensity Gaussian model for each pixel. In recent years, a technique of separating foreground by manually setting thresholds of intensity and color vector difference θ is provided. Compared to a technique in which the foreground is separated based on intensity difference, this technique provides a more accurate result since one more factor is taken into consideration. However, because the color vector difference θ is further calculated regarding each pixel besides the intensity of the pixel, the operation load is greatly increased. As a result, the implementation on an embedded platform is made very complicated.
High accuracy results in high operation load, and high operation load requires high-priced processor, which is concerned to a commercial product because the cost of the product will be increased. In some cases, even the high level processor cannot accomplish the high operation load brought by the complicated calculation algorithm.